




from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase_						 =					3_00  # TEMPERATURE (unit = K)






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__					,						)					->				Dict:





     '''simple docstring'''


     if donor_conc <= 0:
          raise ValueError('''Donor concentration should be positive'''       )
     elif acceptor_conc <= 0:
          raise ValueError('''Acceptor concentration should be positive'''       )
     elif intrinsic_conc <= 0:
          raise ValueError('''Intrinsic concentration should be positive'''       )
     elif donor_conc <= intrinsic_conc:
          raise ValueError(
              '''Donor concentration should be greater than intrinsic concentration'''       )
     elif acceptor_conc <= intrinsic_conc:
          raise ValueError(
              '''Acceptor concentration should be greater than intrinsic concentration'''       )
     else:
          return (
              Boltzmann
              * T
              * log((donor_conc * acceptor_conc) / intrinsic_conc**2       )
              / physical_constants["electron volt"][0]
          )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



